AUTHOR=Villacrés Micaela , Avila Alec , Jimenes-Vargas Karina , Machado António , Alvarez-Suarez José M. , Tejera Eduardo 

TITLE=Discovering molecules and plants with potential activity against gastric cancer: an in silico ensemble-based modeling analysis

JOURNAL=Frontiers in Bioinformatics

VOLUME=Volume 5 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1642039

DOI=10.3389/fbinf.2025.1642039

ISSN=2673-7647

ABSTRACT=BackgroundGastric cancer (GC) remains a major global health burden despite advances in diagnosis and treatment. In recent years, natural products have gained increasing attention as promising sources of anticancer agents, including GC.MethodsIn this study, we applied an in silico ensemble-based modeling strategy to predict compounds with potential inhibitory effects against four GC-related cell lines: AGS, NCI-N87, BGC-823, and SNU-16. Individual predictive models were developed using several algorithms and further integrated into two consensus ensemble multi-objective models. A comprehensive database of over 100,000 natural compounds from 21,665 plant species, was screened for validation and to identify potential molecular candidates.ResultsThe ensemble models demonstrated a 12–15-fold improvement in identifying active molecules compared to random selection. A total of 340 molecules were prioritized, many belonging to bioactive classes such as taxane diterpenoids, flavonoids, isoflavonoids, phloroglucinols, and tryptophan alkaloids. Known anticancer compounds, including paclitaxel, orsaponin (OSW-1), glycybenzofuran, and glyurallin A, were successfully retrieved, reinforcing the validity of the approach. Species from the genera Taxus, Glycyrrhiza, Elaphoglossum, and Seseli emerged as particularly relevant sources of bioactive candidates.ConclusionWhile some genera, such as Taxus and Glycyrrhiza, have well-documented anticancer properties, others, including Elaphoglossum and Seseli, require further experimental validation. These findings highlight the potential of combining multi-objectives ensemble modeling with natural product databases to discover novel phytochemicals relevant to GC treatment.